The Science of Smarter Predictions : Sports analysis

About the Course:

The field of predictive analysis in computer science is a rapidly developing area within Artificial Intelligence. Predictive analysis (predictive analytics) refers to the use of algorithms, data, and computational models to forecast future outcomes based on historical data.

This course introduces the fundamentals of predictive analysis through applications in sports analytics and prediction systems. For instance, can we estimate the probability of a team winning based on past performance and gameplay data?

The programme is designed for students interested in Statistics, Probability Theory, Data Science, and Economics—as well as those curious about how platforms such as Kalshi, Polymarket, or ESPN Analytics operate.

Through this programme, students will engage directly with a research-driven environment—building predictive models, exploring new markets, and developing features that can be deployed on a live platform.

Lead Mentor:

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Nwanacho Nwana is a graduate from the Massacutues Insttute of Technology, an independent researcher and author. He has previously received grant funding from MIT's Integrated Learning Initiative and has a track record of presenting at MIT and Columbia conferences.

Starting Date:

April 25, 2026

Eligibility:

High school and above, with demonstrated interest in Computer Science and Probability.

Timings:

Mondays, Tuesdays and Thursdays | 6.30 PM IST | 1.5 hours

Application deadline

April 20 , 2026.

Register

Prerequisites

Students are expected to have a foundational background in computer science, along with a working knowledge of Python and its core libraries (such as NumPy and pandas). A basic understanding of probability theory and statistics is essential, as the course builds on these concepts to develop and evaluate predictive models.

Who This Is For

This course is designed for students who are curious about:

Statistics, probability, and data science
Economics, forecasting, and prediction markets
How platforms like Kalshi, Polymarket, or ESPN Analytics operate
Building systems that go live and generate real-world signals—not just static reports
Conducting research that can be published, cited, or extended into future academic or professional work

Project Focus:

In the duration of the course, you are required to. Select any one market to create prediction models for:

Nwanacho Nwana | Lead Mentor

Nwanacho Nwana is an educator, researcher, and writer who currently teaches mathematics at the District of Columbia International School (DCI). He is deeply passionate about leveraging data and AI to address educational challenges, particularly in public speaking anxiety and learning equity. As co-founder and Head of Product at Valfee, he has built an app-based platform that uses AI to help students and young entrepreneurs improve through mentorship, deliberate practice, and observational learning. He is a strong advocate for neurodivergent and underrepresented students and integrates his scholarly approach with social impact

Prabigya Acharya | Supporting Mentor

Nwanacho Nwana is an educator, researcher, and writer who currently teaches mathematics at the District of Columbia International School (DCI). He is deeply passionate about leveraging data and AI to address educational challenges, particularly in public speaking anxiety and learning equity. As co-founder and Head of Product at Valfee, he has built an app-based platform that uses AI to help students and young entrepreneurs improve through mentorship, deliberate practice, and observational learning. He is a strong advocate for neurodivergent and underrepresented students and integrates his scholarly approach with social impact

Chirag Dahal | Supporting Mentor

Nwanacho Nwana is an educator, researcher, and writer who currently teaches mathematics at the District of Columbia International School (DCI). He is deeply passionate about leveraging data and AI to address educational challenges, particularly in public speaking anxiety and learning equity. As co-founder and Head of Product at Valfee, he has built an app-based platform that uses AI to help students and young entrepreneurs improve through mentorship, deliberate practice, and observational learning. He is a strong advocate for neurodivergent and underrepresented students and integrates his scholarly approach with social impact

In 2024, Kalshi became the first federally regulated prediction market in the United States, allowing people to trade contracts on real-world outcomes, elections, economic indicators, sports results, and more. Polymarket processed over $3 billion in volume during the 2024 election cycle. Platforms like these are not going away.

They are growing into a new layer of how the world prices uncertainty. And yet most of the participants in these markets from casual users to professional traders are working with gut feel, incomplete data, or models that have never been validated. The question this program asks is simple:

"Can a transparent, walk-forward validated statistical model beat the market consistently, and with documented proof?"

Real Research

This course build's on Kuhiro Class' Basketball IQ project which has already answered this question for college basketball: yes. Across 1,544 games, the Basketball IQ model outperformed Kalshi's predictions by 1.5 percentage points (70.1% vs 68.6%).

That result is time-stamped, walk-forward validated, and publicly verifiable. It has been presented at the MIT GenAI Conference and Columbia's Learning Ideas Conference.The gap we are now trying to close is coverage. One sport has been proven. Twelve more are waiting. That is the research mandate for this program.

Student Evaluation & Certification

Students will receive a letter of engagement upon completing the project, and mentors will evaluate their performance to issue an end-of-project recommendation letter where appropriate. Throughout the program, mentors will assess each student’s engagement during group sessions, the quality and timeliness of their assigned work, and their creativity in developing solutions to group tasks.

Patent and Copyrights

All intellectual property, copyrights, and potential patents arising from this project will be owned solely by the mentor. Distribution of any ownership rights, authorship credit, or derivative use permissions to participating group members will be at the mentor’s discretion and may be allocated based on class performance, contribution level, and the overall quality of engagement throughout the project.

Register now

Sign up for this course by using the registration link below. Seats are limited to between 8-12 and guaged based on student's past perfomances.

Register Here

Fee

NPR 38,000

Up to two scholarships of 100% are available based on the answers provided in the response form. The course fee covers three days of classes per week.

Kuhiro Class

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